options(warn=-1)library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(plotly)## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(rworldmap)## Loading required package: sp## ### Welcome to rworldmap ##### For a short introduction type : vignette('rworldmap')library(maps)
library(ggmap)##
## Attaching package: 'ggmap'## The following object is masked from 'package:plotly':
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## windlibrary(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
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## smithslibrary(raster)##
## Attaching package: 'raster'## The following object is masked from 'package:plotly':
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## select## The following object is masked from 'package:dplyr':
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## select## The following object is masked from 'package:tidyr':
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## extractlibrary(rgdal)## rgdal: version: 1.2-8, (SVN revision 663)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.1.3, released 2017/20/01
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/gdal
## Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/proj
## Linking to sp version: 1.2-4library(rgeos)## rgeos version: 0.3-23, (SVN revision 546)
## GEOS runtime version: 3.6.1-CAPI-1.10.1 r0
## Linking to sp version: 1.2-4
## Polygon checking: TRUEterr = read.csv('~/Downloads/globalterrorismdb_0617dist.csv', check.names = FALSE, header = TRUE, stringsAsFactors = FALSE)summary(terr)## eventid iyear imonth iday
## Min. :1.970e+11 Min. :1970 Min. : 0.000 Min. : 0.00
## 1st Qu.:1.990e+11 1st Qu.:1990 1st Qu.: 4.000 1st Qu.: 8.00
## Median :2.007e+11 Median :2007 Median : 6.000 Median :15.00
## Mean :2.002e+11 Mean :2002 Mean : 6.474 Mean :15.47
## 3rd Qu.:2.014e+11 3rd Qu.:2014 3rd Qu.: 9.000 3rd Qu.:23.00
## Max. :2.017e+11 Max. :2016 Max. :12.000 Max. :31.00
##
## approxdate extended resolution country
## Length:170350 Min. :0.00000 Length:170350 Min. : 4.0
## Class :character 1st Qu.:0.00000 Class :character 1st Qu.: 75.0
## Mode :character Median :0.00000 Mode :character Median : 98.0
## Mean :0.04363 Mean : 132.5
## 3rd Qu.:0.00000 3rd Qu.: 160.0
## Max. :1.00000 Max. :1004.0
##
## country_txt region region_txt provstate
## Length:170350 Min. : 1.000 Length:170350 Length:170350
## Class :character 1st Qu.: 5.000 Class :character Class :character
## Mode :character Median : 6.000 Mode :character Mode :character
## Mean : 7.091
## 3rd Qu.:10.000
## Max. :12.000
##
## city latitude longitude specificity
## Length:170350 Min. :-53.16 Min. :-176.176 Min. :1.000
## Class :character 1st Qu.: 11.26 1st Qu.: 2.396 1st Qu.:1.000
## Mode :character Median : 31.47 Median : 43.130 Median :1.000
## Mean : 23.40 Mean : 26.351 Mean :1.454
## 3rd Qu.: 34.74 3rd Qu.: 68.451 3rd Qu.:1.000
## Max. : 74.63 Max. : 179.367 Max. :5.000
## NA's :4606 NA's :4606 NA's :4
## vicinity location summary crit1
## Min. :-9.00000 Length:170350 Length:170350 Min. :0.0000
## 1st Qu.: 0.00000 Class :character Class :character 1st Qu.:1.0000
## Median : 0.00000 Mode :character Mode :character Median :1.0000
## Mean : 0.06779 Mean :0.9884
## 3rd Qu.: 0.00000 3rd Qu.:1.0000
## Max. : 1.00000 Max. :1.0000
##
## crit2 crit3 doubtterr alternative
## Min. :0.000 Min. :0.0000 Min. :-9.0000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.: 0.0000 1st Qu.:1.00
## Median :1.000 Median :1.0000 Median : 0.0000 Median :1.00
## Mean :0.993 Mean :0.8798 Mean :-0.5721 Mean :1.29
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.: 0.0000 3rd Qu.:1.00
## Max. :1.000 Max. :1.0000 Max. : 1.0000 Max. :5.00
## NA's :143719
## alternative_txt multiple success suicide
## Length:170350 Min. :0.0000 Min. :0.0000 Min. :0.00000
## Class :character 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.00000
## Mode :character Median :0.0000 Median :1.0000 Median :0.00000
## Mean :0.1342 Mean :0.8964 Mean :0.03387
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.00000
##
## attacktype1 attacktype1_txt attacktype2 attacktype2_txt
## Min. :1.00 Length:170350 Min. :1.00 Length:170350
## 1st Qu.:2.00 Class :character 1st Qu.:2.00 Class :character
## Median :3.00 Mode :character Median :2.00 Mode :character
## Mean :3.22 Mean :3.67
## 3rd Qu.:3.00 3rd Qu.:6.00
## Max. :9.00 Max. :9.00
## NA's :164720
## attacktype3 attacktype3_txt targtype1 targtype1_txt
## Min. :1.00 Length:170350 Min. : 1.0 Length:170350
## 1st Qu.:2.00 Class :character 1st Qu.: 3.0 Class :character
## Median :6.00 Mode :character Median : 4.0 Mode :character
## Mean :5.11 Mean : 8.4
## 3rd Qu.:7.00 3rd Qu.:14.0
## Max. :7.00 Max. :22.0
## NA's :169976
## targsubtype1 targsubtype1_txt corp1 target1
## Min. : 1.00 Length:170350 Length:170350 Length:170350
## 1st Qu.: 22.00 Class :character Class :character Class :character
## Median : 34.00 Mode :character Mode :character Mode :character
## Mean : 46.87
## 3rd Qu.: 74.00
## Max. :111.00
## NA's :9345
## natlty1 natlty1_txt targtype2 targtype2_txt
## Min. : 4.0 Length:170350 Min. : 1.0 Length:170350
## 1st Qu.: 83.0 Class :character 1st Qu.: 4.0 Class :character
## Median : 102.0 Mode :character Median :14.0 Mode :character
## Mean : 127.7 Mean :10.2
## 3rd Qu.: 175.0 3rd Qu.:14.0
## Max. :1004.0 Max. :22.0
## NA's :1394 NA's :160332
## targsubtype2 targsubtype2_txt corp2 target2
## Min. : 1.00 Length:170350 Length:170350 Length:170350
## 1st Qu.: 14.00 Class :character Class :character Class :character
## Median : 14.00 Mode :character Mode :character Mode :character
## Mean : 28.37
## 3rd Qu.: 47.00
## Max. :109.00
## NA's :160749
## natlty2 natlty2_txt targtype3 targtype3_txt
## Min. : 4 Length:170350 Min. : 1.00 Length:170350
## 1st Qu.: 92 Class :character 1st Qu.: 3.00 Class :character
## Median : 98 Mode :character Median :14.00 Mode :character
## Mean : 132 Mean : 9.88
## 3rd Qu.: 182 3rd Qu.:14.00
## Max. :1004 Max. :22.00
## NA's :160644 NA's :169316
## targsubtype3 targsubtype3_txt corp3 target3
## Min. : 1.00 Length:170350 Length:170350 Length:170350
## 1st Qu.: 26.00 Class :character Class :character Class :character
## Median : 67.00 Mode :character Mode :character Mode :character
## Mean : 55.07
## 3rd Qu.: 73.00
## Max. :109.00
## NA's :169389
## natlty3 natlty3_txt gname gsubname
## Min. : 4.0 Length:170350 Length:170350 Length:170350
## 1st Qu.: 74.0 Class :character Class :character Class :character
## Median : 104.0 Mode :character Mode :character Mode :character
## Mean : 139.6
## 3rd Qu.: 182.0
## Max. :1004.0
## NA's :169344
## gname2 gsubname2 gname3
## Length:170350 Length:170350 Length:170350
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## gsubname3 motive guncertain1 guncertain2
## Length:170350 Length:170350 Min. :0.0000 Min. :0.00
## Class :character Class :character 1st Qu.:0.0000 1st Qu.:0.00
## Mode :character Mode :character Median :0.0000 Median :0.00
## Mean :0.0844 Mean :0.26
## 3rd Qu.:0.0000 3rd Qu.:1.00
## Max. :1.0000 Max. :1.00
## NA's :379 NA's :168669
## guncertain3 individual nperps nperpcap
## Min. :0.00 Min. :0.000000 Min. : -99.00 Min. :-99.00
## 1st Qu.:0.00 1st Qu.:0.000000 1st Qu.: -99.00 1st Qu.: 0.00
## Median :0.00 Median :0.000000 Median : -99.00 Median : 0.00
## Mean :0.18 Mean :0.002483 Mean : -63.92 Mean : -1.48
## 3rd Qu.:0.00 3rd Qu.:0.000000 3rd Qu.: 1.00 3rd Qu.: 0.00
## Max. :1.00 Max. :1.000000 Max. :25000.00 Max. :406.00
## NA's :170085 NA's :71124 NA's :69499
## claimed claimmode claimmode_txt claim2
## Min. :-9.00 Min. : 1.00 Length:170350 Min. :-9.00
## 1st Qu.: 0.00 1st Qu.: 6.00 Class :character 1st Qu.: 0.00
## Median : 0.00 Median : 8.00 Mode :character Median : 0.00
## Mean : 0.03 Mean : 7.01 Mean : 0.24
## 3rd Qu.: 0.00 3rd Qu.: 9.00 3rd Qu.: 1.00
## Max. : 1.00 Max. :10.00 Max. : 1.00
## NA's :66134 NA's :154102 NA's :168733
## claimmode2 claimmode2_txt claim3 claimmode3
## Min. : 1.00 Length:170350 Min. :0.00 Min. : 1.00
## 1st Qu.: 6.00 Class :character 1st Qu.:0.00 1st Qu.: 5.50
## Median : 7.00 Mode :character Median :0.00 Median : 8.00
## Mean : 7.22 Mean :0.45 Mean : 7.09
## 3rd Qu.:10.00 3rd Qu.:1.00 3rd Qu.:10.00
## Max. :10.00 Max. :1.00 Max. :10.00
## NA's :169818 NA's :170087 NA's :170230
## claimmode3_txt compclaim weaptype1 weaptype1_txt
## Length:170350 Min. :-9.00 Min. : 1.000 Length:170350
## Class :character 1st Qu.:-9.00 1st Qu.: 5.000 Class :character
## Mode :character Median :-9.00 Median : 6.000 Mode :character
## Mean :-6.42 Mean : 6.429
## 3rd Qu.: 0.00 3rd Qu.: 6.000
## Max. : 1.00 Max. :13.000
## NA's :165595
## weapsubtype1 weapsubtype1_txt weaptype2 weaptype2_txt
## Min. : 1.00 Length:170350 Min. : 1.00 Length:170350
## 1st Qu.: 5.00 Class :character 1st Qu.: 5.00 Class :character
## Median :12.00 Mode :character Median : 6.00 Mode :character
## Mean :11.02 Mean : 6.74
## 3rd Qu.:16.00 3rd Qu.: 8.00
## Max. :30.00 Max. :13.00
## NA's :19426 NA's :158507
## weapsubtype2 weapsubtype2_txt weaptype3 weaptype3_txt
## Min. : 1.00 Length:170350 Min. : 2.00 Length:170350
## 1st Qu.: 5.00 Class :character 1st Qu.: 5.00 Class :character
## Median : 7.00 Mode :character Median : 6.00 Mode :character
## Mean :10.67 Mean : 6.87
## 3rd Qu.:18.00 3rd Qu.: 8.00
## Max. :30.00 Max. :13.00
## NA's :159875 NA's :168690
## weapsubtype3 weapsubtype3_txt weaptype4 weaptype4_txt
## Min. : 1.00 Length:170350 Min. : 5.00 Length:170350
## 1st Qu.: 4.00 Class :character 1st Qu.: 5.00 Class :character
## Median : 7.00 Mode :character Median : 6.00 Mode :character
## Mean :11.51 Mean : 6.24
## 3rd Qu.:19.00 3rd Qu.: 6.00
## Max. :28.00 Max. :12.00
## NA's :168843 NA's :170276
## weapsubtype4 weapsubtype4_txt weapdetail nkill
## Min. : 2.00 Length:170350 Length:170350 Min. : 0.000
## 1st Qu.: 3.00 Class :character Class :character 1st Qu.: 0.000
## Median : 8.00 Mode :character Mode :character Median : 0.000
## Mean :10.79 Mean : 2.387
## 3rd Qu.:16.00 3rd Qu.: 2.000
## Max. :28.00 Max. :1500.000
## NA's :170279 NA's :9682
## nkillus nkillter nwound nwoundus
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.00
## Median : 0.00 Median : 0.00 Median : 0.0 Median : 0.00
## Mean : 0.05 Mean : 0.48 Mean : 3.2 Mean : 0.04
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 2.0 3rd Qu.: 0.00
## Max. :1358.00 Max. :500.00 Max. :7366.0 Max. :751.00
## NA's :64459 NA's :66663 NA's :15325 NA's :64710
## nwoundte property propextent propextent_txt
## Min. : 0.0 Min. :-9.000 Min. :1.0 Length:170350
## 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.:3.0 Class :character
## Median : 0.0 Median : 1.000 Median :3.0 Mode :character
## Mean : 0.1 Mean :-0.506 Mean :3.3
## 3rd Qu.: 0.0 3rd Qu.: 1.000 3rd Qu.:4.0
## Max. :200.0 Max. : 1.000 Max. :4.0
## NA's :68749 NA's :109805
## propvalue propcomment ishostkid
## Min. : -99 Length:170350 Min. :-9.00000
## 1st Qu.: -99 Class :character 1st Qu.: 0.00000
## Median : -99 Mode :character Median : 0.00000
## Mean : 229057 Mean : 0.05918
## 3rd Qu.: 2500 3rd Qu.: 0.00000
## Max. :2700000000 Max. : 1.00000
## NA's :134863 NA's :178
## nhostkid nhostkidus nhours ndays
## Min. : -99.00 Min. :-99.00 Min. :-99.00 Min. : -99.00
## 1st Qu.: 1.00 1st Qu.: 0.00 1st Qu.:-99.00 1st Qu.: -99.00
## Median : 2.00 Median : 0.00 Median : 0.00 Median : -99.00
## Mean : 4.51 Mean : -0.38 Mean :-42.75 Mean : -31.89
## 3rd Qu.: 4.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 4.00
## Max. :17000.00 Max. : 86.00 Max. :999.00 Max. :2454.00
## NA's :157858 NA's :157913 NA's :166649 NA's :162942
## divert kidhijcountry ransom
## Length:170350 Length:170350 Min. :-9.00
## Class :character Class :character 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.00
## Mean :-0.14
## 3rd Qu.: 0.00
## Max. : 1.00
## NA's :94037
## ransomamt ransomamtus ransompaid
## Min. : -99 Min. : -99 Min. : -99
## 1st Qu.: 0 1st Qu.: 0 1st Qu.: -99
## Median : 14200 Median : 0 Median : 0
## Mean : 3224502 Mean : 451992 Mean : 384966
## 3rd Qu.: 400000 3rd Qu.: 0 3rd Qu.: 736
## Max. :1000000000 Max. :132000000 Max. :41000000
## NA's :169071 NA's :169854 NA's :169643
## ransompaidus ransomnote hostkidoutcome hostkidoutcome_txt
## Min. : -99.0 Length:170350 Min. :1.00 Length:170350
## 1st Qu.: 0.0 Class :character 1st Qu.:2.00 Class :character
## Median : 0.0 Mode :character Median :4.00 Mode :character
## Mean : 272.5 Mean :4.62
## 3rd Qu.: 0.0 3rd Qu.:7.00
## Max. :48000.0 Max. :7.00
## NA's :169863 NA's :160439
## nreleased addnotes scite1
## Min. : -99.00 Length:170350 Length:170350
## 1st Qu.: -99.00 Class :character Class :character
## Median : 0.00 Mode :character Mode :character
## Mean : -28.72
## 3rd Qu.: 1.00
## Max. :1201.00
## NA's :161028
## scite2 scite3 dbsource INT_LOG
## Length:170350 Length:170350 Length:170350 Min. :-9.000
## Class :character Class :character Class :character 1st Qu.:-9.000
## Mode :character Mode :character Mode :character Median :-9.000
## Mean :-4.583
## 3rd Qu.: 0.000
## Max. : 1.000
##
## INT_IDEO INT_MISC INT_ANY related
## Min. :-9.000 Min. :-9.00000 Min. :-9.000 Length:170350
## 1st Qu.:-9.000 1st Qu.: 0.00000 1st Qu.:-9.000 Class :character
## Median :-9.000 Median : 0.00000 Median : 0.000 Mode :character
## Mean :-4.511 Mean : 0.09108 Mean :-3.975
## 3rd Qu.: 0.000 3rd Qu.: 0.00000 3rd Qu.: 0.000
## Max. : 1.000 Max. : 1.00000 Max. : 1.000
## c(ncol(terr),nrow(terr))## [1] 135 170350sum(is.na(terr))#/(ncol(data)*nrow(data))## [1] 6322260terr=rename(terr, id=eventid, year=iyear, nation=country_txt,
Region=region_txt, attack=attacktype1_txt,
target=targtype1_txt, weapon=weaptype1_txt,
Killed=nkill, wounded=nwound)We clean the data
terr$Killed=as.integer(terr$Killed)
terr$wounded=as.integer(terr$wounded)
terr$Killed[which(is.na(terr$Killed))] = 0
terr$wounded[which(is.na(terr$wounded))] = 0
terr$nation[terr$nation=="United States"] <- "USA"
terr$nation[terr$nation=="United Kingdom"] <- "UK"
terr$nation[terr$nation=="People's Republic of the Congo"] <- "Republic of Congo"
terr$nation[terr$nation=="Bosnia-Herzegovina"] <- "Bosnia and Herzegovina"
terr$nation[terr$nation=="Slovak Republic"] <- "Slovakia"global_t <-
terr %>%
group_by(year,nation,Region) %>%
summarize(Total=n())
global_y <- global_t%>%group_by(year)%>%summarize(Total=sum(Total))
global_attacks <-
global_t %>%
group_by(nation) %>%
summarize(Total=sum(Total)) %>%
arrange(desc(Total))
#%>%filter(Total>300)
#global_attacks$Total[which(is.na(global_attacks$Total))] = 0
attach(global_attacks)
global_n <- global_attacks[order(-Total),]
detach(global_attacks)Let’s look at the number of terrorist attacks with the passage of time.
gy <- global_y %>%
ggplot(mapping=aes(year,Total))+
geom_line(color="red")+
#geom_bar(mapping=aes(x=year,y=Total,fill=year), stat='identity')+
theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=45, vjust = 1))+
labs(x="Year", y="Number of attacks", title="Number of global attacks over years")
ggplotly(gy, width = 800, height=480)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`global_kills_years <- terr%>%group_by(year)%>%summarize(killed=sum(Killed))
global_wound_years <- terr%>%group_by(year)%>%summarize(wounded=sum(wounded))
globe <-
global_kills_years %>%
inner_join(global_wound_years, by="year")
#global_kills_years <- global_kills_years%>%group_by(year)%>%summarize(Total=sum(Total))
#head(globe)
df <- melt(globe, "year")
df=rename(df, effect=variable)
gky <- df %>%
ggplot(mapping=aes(x=year,y=value, color=effect))+
geom_line()+
theme(panel.background = NULL, axis.text.x = element_text(angle=45, vjust = 1))+
labs(x="Year", y="Count", title="Number of people killed/wounded over years")
ggplotly(gky, width = 800, height=450)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`world <- map_data("world")
global <- rename(terr, long=longitude, lat=latitude)#helper theme for common visualizations
ditch_the_axes <- theme(
axis.text = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.title = element_blank()
)#helper function to visualize maps for varaibles for counts
visualize_count_map <- function(global_table, title){
global_table <-
global_table %>%
rename(region=nation)
#join the data found with world data
world_table <- right_join(global_table, world, by="region")
world_table$Total[which(is.na(world_table$Total))]=0
g <- ggplot(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
ditch_the_axes+
theme(legend.position = "none", panel.background = NULL)+
geom_polygon(data = world_table, aes(label = region, fill = Total), color ="white") +
scale_fill_gradientn(colours=c("#e1eec3","#f05053"))+
ggtitle(title)
ggplotly(g, width = 850, height = 480)
}visualize_count_map(global_attacks, "Number of terrorist attacks around the world")## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`global_kills <-
global %>%
group_by(nation) %>%
summarise(Total = sum(Killed))
visualize_count_map(global_kills, "Number of people killed around the world")## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`global_wound <-
global %>%
group_by(nation) %>%
summarise(Total = sum(wounded))
visualize_count_map(global_wound, "Number of people wounded around the world")## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`#get weapon most used in each nation
global_weapons_max_type <-
terr[c('nation', 'weapon')] %>%
group_by(nation, weapon) %>%
summarize(Total=n()) %>%
filter(Total==max(Total)) %>%
group_by(nation)
global_weapons_max_type <-
global_weapons_max_type %>%
rename(region=nation)
#join the data found with world data
world_weapons <- right_join(global_weapons_max_type, world, by="region")
world_weapons$weapon[which(is.na(world_weapons$weapon))]="Unknown"num_unique_weapons <- length(unique(world_weapons$weapon))
g4 <- ggplot(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
ditch_the_axes+
theme(panel.background = NULL)+
geom_polygon(data = world_weapons, aes(label = region, fill = factor(weapon)), color ="white") +
scale_fill_manual(values = c(rainbow(num_unique_weapons,
s = 0.6,
v = 0.8, start = 0,
end = max(1, num_unique_weapons - 1)/num_unique_weapons,
alpha = 0.7)))+
ggtitle("Most used weapons in terrorist attacks around the world")
ggplotly(g4, width = 850, height = 480)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`visualize_factor_map <- function(global_table, factor, title) {
global_attack_max_type <-
global_attack_max_type %>%
rename(region=nation)
#join the data found with world data
world_attack_types <- right_join(global_attack_max_type, world, by="region")
world_attack_types$attack[which(is.na(world_attack_types$attack))]="Unknown"
num_unique_attacks <- length(unique(global_attack_max_type$attack))
g5 <- ggplot(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
ditch_the_axes+
theme(panel.background = NULL)+
geom_polygon(data = world_attack_types, aes(label = region, fill = factor(attack)), color ="white") +
scale_fill_manual(values = c(rainbow(num_unique_attacks,
s = 0.6,
v = 0.8, start = 0,
end = max(1, num_unique_attacks - 1)/num_unique_attacks,
alpha = 0.7)))+
ggtitle("Most particular type of attacks in different countries")
ggplotly(g5, width = 900, height = 480)
}#get weapon most used in each nation
global_attack_max_type <-
terr[c('nation', 'attack')] %>%
group_by(nation, attack) %>%
summarize(Total=n()) %>%
filter(Total==max(Total)) %>%
group_by(nation)
global_attack_max_type <-
global_attack_max_type %>%
rename(region=nation)
#join the data found with world data
world_attack_types <- right_join(global_attack_max_type, world, by="region")
world_attack_types$attack[which(is.na(world_attack_types$attack))]="Unknown"num_unique_attacks <- length(unique(global_attack_max_type$attack))
g5 <- ggplot(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
ditch_the_axes+
theme(panel.background = NULL)+
geom_polygon(data = world_attack_types, aes(label = region, fill = factor(attack)), color ="white") +
scale_fill_manual(values = c(rainbow(num_unique_attacks,
s = 0.6,
v = 0.8, start = 0,
end = max(1, num_unique_attacks - 1)/num_unique_attacks,
alpha = 0.7)))+
ggtitle("Most particular type of attacks in different countries")
ggplotly(g5, width = 900, height = 480)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`#get weapon most used in each nation
global_target_max_type <-
terr[c('nation', 'target')] %>%
group_by(nation, target) %>%
summarize(Total=n()) %>%
filter(Total==max(Total)) %>%
group_by(nation)
global_target_max_type <-
global_target_max_type %>%
rename(region=nation)
#join the data found with world data
world_target_types <- right_join(global_target_max_type, world, by="region")
world_target_types$target[which(is.na(world_target_types$target))]="Unknown"num_unique_targets <- length(unique(global_target_max_type$target))
g6 <- ggplot(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
ditch_the_axes+
theme(panel.background = NULL)+
geom_polygon(data = world_target_types, aes(label = region, fill = factor(target)), color ="white") +
scale_fill_manual(values=c(terrain.colors(num_unique_targets, alpha = 0.8)))+
ggtitle("Most particular type of targets in different countries")
ggplotly(g6, width = 900, height = 480)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`#get weapon most used in each nation
global_group_max_attacks <-
terr[c('nation', 'gname')] %>%
group_by(nation, gname) %>%
summarize(Total=n()) %>%
filter(gname!= "Unknown") %>%
filter(Total==max(Total)) %>%
group_by(nation)
global_group_max_attacks <-
global_group_max_attacks %>%
rename(region=nation)
#join the data found with world data
world_groups_attacks <- right_join(global_group_max_attacks, world, by="region")
world_groups_attacks$gname[which(is.na(world_groups_attacks$gname))]="Unknown"num_unique_groups <- length(unique(global_group_max_attacks$gname))
terrorist_group <- factor(world_groups_attacks$gname)
g7 <- ggplot(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
ditch_the_axes+
theme(legend.position = "none", panel.background = NULL)+
geom_polygon(data = world_groups_attacks, aes(label = region, fill = terrorist_group), color ="white") +
scale_fill_manual(values=c(topo.colors(num_unique_groups, alpha = 0.6)))+
ggtitle("Most active terrorist groups around the globe")
ggplotly(g7, width = 850, height = 480)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`#get weapon most used in each nation
global_int_attacks <-
terr[c('nation', 'INT_ANY')] %>%
filter(INT_ANY=='1') %>%
group_by(nation) %>%
summarize(Total=n())
visualize_count_map(global_int_attacks, "International attacks around the globe")## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`#get weapon most used in each nation
global_dom_attacks <-
terr[c('nation', 'INT_ANY')] %>%
filter(INT_ANY=='0') %>%
group_by(nation) %>%
summarize(Total=n())
visualize_count_map(global_dom_attacks, "Domestic attacks around the globe")## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`